Computer data distribution architecture

ABSTRACT

Described are methods, systems and computer readable media for computer data distribution architecture.

This application is a continuation of U.S. application Ser. No. 15/154,996, entitled “Computer Data Distribution Architecture” and filed on May 14, 2016, which claims the benefit of U.S. Provisional Application No. 62/161,813, entitled “Computer Data System” and filed on May 14, 2015, which is incorporated herein by reference in its entirety.

Embodiments relate generally to computer data systems, and more particularly, to methods, systems and computer readable media for computer data distribution architecture.

Some conventional computer data systems may maintain data in one or more data sources that may include data objects such as tables. These conventional systems may include clients that independently access tables from each data source directly. In such data systems, a need may exist to provide systems and methods for an optimized composite table data service providing flexible data routing and caching across the various data sources for one or more clients, in order to reduce memory usage and to enable redundancy, high-availability, scalability, and rule-based data discovery.

Embodiments were conceived in light of the above mentioned needs, problems and/or limitations, among other things.

Some implementations can include a computer database system with a plurality of memory devices optimized for ordered data and read-dominated workloads. The system can comprise one or more processors and computer readable storage coupled to the one or more processors, the computer readable storage having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations can include storing at a first data server computer first data in a first memory device in a first column-oriented configuration. The operations can also include storing at a second data server computer second data in a second memory device in a second column-oriented configuration. The operations can further include storing at a third data server computer the third data in a third memory device in a third column-oriented configuration. The operations can also include executing, at a query server computer, a database query, the query server computer comprising a query server memory device and being coupled to the first, second, and third data server computers.

The executing can include accessing a table comprising first and second columns, one or more first rows, and one or more second rows. The executing can also include storing one or more first location identifiers indicating where column data of the first column is stored and one or more second location identifiers indicating where column data of the second column is stored. The one or more first location identifiers can indicate that data of the one or more first rows of the first column is stored in the first memory device of the first data server computer. The one or more second location identifiers can indicate that data of the one or more first rows of the second column is stored in the second memory device of the second data server computer and that data of the one or more second rows of the second column is stored in the third memory device of the third data server computer. The executing can further include storing a table index comprising, for each location identifier of the one or more first location identifiers and the one or more second location identifiers, an index identifier indicating a valid portion of the data at the location indicated by the location identifier. The first memory device, the second memory device, and the third memory device can all be different from each other.

The executing can also include requesting one or more blocks of data of the first column from the first data server computer and requesting one or more blocks of data of the second column from the second or third data server computer. The executing can further include requesting one or more blocks of data of the second column from the second data server computer and requesting one or more blocks of data of the second column from the third data server computer. The executing can also include requesting one or more blocks of data of the one or more first rows from the first or second data server computers and requesting one or more blocks of data of the one or more second rows from the third data server computer. The executing can further include requesting one or more blocks of data of the one or more first rows from the first data server computer and requesting one or more blocks of data of the one or more first rows from the second data server computer. The first memory device can be a transitory memory device and the second memory device can be a persistent memory device. The one or more first location identifiers can indicate that data of the one or more second rows of the first column is stored in the query server memory device of the query server computer.

Some implementations can include a method optimized for ordered data and read-dominated workloads of a computer data system with a plurality of memory devices. The method can include storing at a first data server computer first data in a first memory device in a first column-oriented configuration. The method can also include storing at a second data server computer second data in a second memory device in a second column-oriented configuration. The method can further include storing at a third data server computer the third data in a third memory device in a third column-oriented configuration. The method can also include executing, at a query server computer, a database query, the query server computer comprising a query server memory device and being coupled to the first, second, and third data server computers.

The executing can include accessing a table comprising first and second columns, one or more first rows, and one or more second rows. The executing can also include storing one or more first location identifiers indicating where column data of the first column is stored and one or more second location identifiers indicating where column data of the second column is stored. The one or more first location identifiers can indicate that data of the one or more first rows of the first column is stored in the first memory device of the first data server computer. The one or more second location identifiers can indicate that data of the one or more first rows of the second column is stored in the second memory of the second data server computer and that data of the one or more second rows of the second column is stored in the third memory of the third data server computer. The executing can further include storing a table index comprising, for each location identifier of the one or more first location identifiers and the one or more second location identifiers, an index identifier indicating a valid portion of the data at the location indicated by the location identifier.

The executing can also include requesting one or more blocks of data of the first column from the first data server computer and requesting one or more blocks of data of the second column from the second or third data server computer. The executing can further include requesting one or more blocks of data of the second column from the second data server computer and requesting one or more blocks of data of the second column from the third data server computer. The executing can also include requesting one or more blocks of data of the one or more first rows from the first or second data server computers and requesting one or more blocks of data of the one or more second rows from the third data server computer. The executing can further include requesting one or more blocks of data of the one or more first rows from the first data server computer and requesting one or more blocks of data of the one or more first rows from the second data server computer. The first memory device can be a transitory memory device and the second memory device can be a persistent memory device. The first memory device, the second memory device, and the third memory device can all be different from each other. The one or more first location identifiers can indicate that data of the one or more second rows of the first column is stored in the query server memory device of the query server computer.

Some implementations can include a nontransitory computer readable medium having stored thereon software instructions that, when executed by one or more processors, cause the processors to perform operations. The operations can include storing at a first data server first data in a first memory device in a first column-oriented configuration. The operations can also include storing at a second data server second data in a second memory device in a second column-oriented configuration. The operations can also include storing at a third data server the third data in a third memory device in a third column-oriented configuration. The operations can further include executing, at a query server, a database query, the query server comprising a query server memory device and being coupled to the first, second, and third data servers.

The executing can include accessing a table comprising first and second columns, one or more first rows, and one or more second rows. The executing can also include storing one or more first location identifiers indicating where column data of the first column is stored and one or more second location identifiers indicating where column data of the second column is stored. The one or more first location identifiers can indicate that data of the one or more first rows of the first column is stored in the first memory device of the first data server. The one or more second location identifiers can indicate that data of the one or more first rows of the second column is stored in the second memory device of the second data server and that data of the one or more second rows of the second column is stored in the third memory device of the third data server. The executing can further include storing a table index comprising, for each location identifier of the one or more first location identifiers and the one or more second location identifiers, an index identifier indicating a valid portion of the data at the location indicated by the location identifier

The executing can also include requesting one or more blocks of data of the first column from the first data server; and requesting one or more blocks of data of the second column from the second or third data server. The executing can further include requesting one or more blocks of data of the second column from the second data server; and requesting one or more blocks of data of the second column from the third data server. The executing can also include requesting one or more blocks of data of the one or more first rows from the first or second data server; and requesting one or more blocks of data of the one or more second rows from the third data server. The executing can further include requesting one or more blocks of data of the one or more first rows from the first data server; and requesting one or more blocks of data of the one or more first rows from the second data server. The first memory device can be a transitory memory device and the second memory device can be a persistent memory device. The first memory device, the second memory device, and the third memory device can all be different from each other. The one or more first location identifiers can indicate that data of the one or more second rows of the first column is stored in the query server memory device of the query server.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example computer data system showing an example data distribution configuration in accordance with some implementations.

FIG. 2 is a diagram of an example computer data system showing an example administration/process control arrangement in accordance with some implementations.

FIG. 3A is a diagram of an example query server host in accordance with some implementations.

FIG. 3B is a diagram of an example query server host in accordance with some implementations.

FIG. 4 is a diagram of an example computer data system and network 400 showing an example data distribution configuration in accordance with some implementations.

FIG. 5 is a flowchart of an example method of processing a TDCP composite table data service request in accordance with some implementations.

FIG. 6 is a flowchart of an example method of processing a table location discovery request by a TDCP server in accordance with some implementations.

FIG. 7 is a flowchart of an example method of processing a table location metadata retrieval request by a TDCP server in accordance with some implementations.

FIG. 8 is a flowchart of an example method of processing a column location metadata retrieval request by a TDCP server in accordance with some implementations.

FIG. 9 is a flowchart of an example method of processing a column file size retrieval request by a TDCP server in accordance with some implementations.

FIG. 10 is a flowchart of an example method of processing a column file data retrieval request by a TDCP server in accordance with some implementations.

FIG. 11 is a diagram of an example computing device configured for table data cache proxy (TDCP) processing in accordance with at least one implementation.

DETAILED DESCRIPTION

Reference may be made herein to the Java programming language, Java classes, Java bytecode and the Java Virtual Machine (JVM) for purposes of illustrating example implementations. It will be appreciated that implementations can include other programming languages (e.g., groovy, Scala, R, Go, etc.), other programming language structures as an alternative to or in addition to Java classes (e.g., other language classes, objects, data structures, program units, code portions, script portions, etc.), other types of bytecode, object code and/or executable code, and/or other virtual machines or hardware implemented machines configured to execute a data system query.

FIG. 1 is a diagram of an example computer data system and network 100 showing an example data distribution configuration in accordance with some implementations. In particular, the system 100 includes an application host 102, a periodic data import host 104, a query server host 106, a long-term file server 108, and a user data import host 110. While tables are used as an example data object in the description below, it will be appreciated that the data system described herein can also process other data objects such as mathematical objects (e.g., a singular value decomposition of values in a given range of one or more rows and columns of a table), TableMap objects, etc. A TableMap object provides the ability to lookup a Table by some key. This key represents a unique value (or unique tuple of values) from the columns aggregated on in a byExternal( ) statement execution, for example. A TableMap object is can be the result of a byExternal( ) statement executed as part of a query. It will also be appreciated that the configurations shown in FIGS. 1 and 2 are for illustration purposes and in a given implementation each data pool (or data store) may be directly attached or may be managed by a file server.

The application host 102 can include one or more application processes 112, one or more log files 114 (e.g., sequential, row-oriented log files), one or more data log tailers 116 and a multicast key-value publisher 118. The periodic data import host 104 can include a local table data server, direct or remote connection to a periodic table data store 122 (e.g., a column-oriented table data store) and a data import server 120. The query server host 106 can include a multicast key-value subscriber 126, a performance table logger 128, local table data store 130 and one or more remote query processors (132, 134) each accessing one or more respective tables (136, 138). The long-term file server 108 can include a long-term data store 140. The user data import host 110 can include a remote user table server 142 and a user table data store 144. Row-oriented log files and column-oriented table data stores are discussed herein for illustration purposes and are not intended to be limiting. It will be appreciated that log files and/or data stores may be configured in other ways. In general, any data stores discussed herein could be configured in a manner suitable for a contemplated implementation.

In operation, the input data application process 112 can be configured to receive input data from a source (e.g., a securities trading data source), apply schema-specified, generated code to format the logged data as it's being prepared for output to the log file 114 and store the received data in the sequential, row-oriented log file 114 via an optional data logging process. In some implementations, the data logging process can include a daemon, or background process task, that is configured to log raw input data received from the application process 112 to the sequential, row-oriented log files on disk and/or a shared memory queue (e.g., for sending data to the multicast publisher 118). Logging raw input data to log files can additionally serve to provide a backup copy of data that can be used in the event that downstream processing of the input data is halted or interrupted or otherwise becomes unreliable.

A data log tailer 116 can be configured to access the sequential, row-oriented log file(s) 114 to retrieve input data logged by the data logging process. In some implementations, the data log tailer 116 can be configured to perform strict byte reading and transmission (e.g., to the data import server 120). The data import server 120 can be configured to store the input data into one or more corresponding data stores such as the periodic table data store 122 in a column-oriented configuration. The periodic table data store 122 can be used to store data that is being received within a time period (e.g., a minute, an hour, a day, etc.) and which may be later processed and stored in a data store of the long-term file server 108. For example, the periodic table data store 122 can include a plurality of data servers configured to store periodic securities trading data according to one or more characteristics of the data (e.g., a data value such as security symbol, the data source such as a given trading exchange, etc.).

The data import server 120 can be configured to receive and store data into the periodic table data store 122 in such a way as to provide a consistent data presentation to other parts of the system. Providing/ensuring consistent data in this context can include, for example, recording logged data to a disk or memory, ensuring rows presented externally are available for consistent reading (e.g., to help ensure that if the system has part of a record, the system has all of the record without any errors), and preserving the order of records from a given data source. If data is presented to clients, such as a remote query processor (132, 134), then the data may be persisted in some fashion (e.g., written to disk).

The local table data server 124 can be configured to retrieve data stored in the periodic table data store 122 and provide the retrieved data to one or more remote query processors (132, 134) via an optional proxy (e.g., table data cache proxy (TDCP) 394 and/or 404 as shown in FIG. 3 and FIG. 4, respectively). Remote query processors (132, 134) can also receive data from DIS 120 and/or LTDS 124 via the proxy.

The remote user table server (RUTS) 142 can include a centralized consistent data writer, as well as a data server that provides processors with consistent access to the data that it is responsible for managing. For example, users can provide input to the system by writing table data that is then consumed by query processors.

The remote query processors (132, 134) can use data from the data import server 120, local table data server 124 and/or from the long-term file server 108 to perform queries. The remote query processors (132, 134) can also receive data from the multicast key-value subscriber 126, which receives data from the multicast key-value publisher 118 in the application host 102. The performance table logger 128 can log performance information about each remote query processor and its respective queries into a local table data store 130. Further, the remote query processors can also read data from the RUTS, from local table data written by the performance logger, or from user table data read over NFS, for example.

It will be appreciated that the configuration shown in FIG. 1 is a typical example configuration that may be somewhat idealized for illustration purposes. An actual configuration may include one or more of each server and/or host type. The hosts/servers shown in FIG. 1 (e.g., 102-110, 120, 124 and 142) may each be separate or two or more servers may be combined into one or more combined server systems. Data stores can include local/remote, shared/isolated and/or redundant. Any table data may flow through optional proxies indicated by an asterisk on certain connections to the remote query processors (e.g., table data cache proxy (TDCP) 392 or 404 as shown in FIG. 3B and FIG. 4, respectively). Also, it will be appreciated that the term “periodic” is being used for illustration purposes and can include, but is not limited to, data that has been received within a given time period (e.g., millisecond, second, minute, hour, day, week, month, year, etc.) and which has not yet been stored to a long-term data store (e.g., 140).

FIG. 2 is a diagram of an example computer data system 200 showing an example administration/process control arrangement in accordance with some implementations. The system 200 includes a production client host 202, a controller host 204, a GUI host or workstation 206, and query server hosts 208 and 210. It will be appreciated that there may be one or more of each of 202-210 in a given implementation.

The production client host 202 can include a batch query application 212 (e.g., a query that is executed from a command line interface or the like) and a real time query data consumer process 214 (e.g., an application that connects to and listens to tables created from the execution of a separate query). The batch query application 212 and the real time query data consumer 214 can connect to a remote query dispatcher 222 and one or more remote query processors (224, 226) within the query server host 1 208.

The controller host 204 can include a persistent query controller 216 configured to connect to a remote query dispatcher 232 and one or more remote query processors 228-230. In some implementations, the persistent query controller 216 can serve as the “primary client” for persistent queries and can request remote query processors from dispatchers, and send instructions to start persistent queries. For example, a user can submit a query to 216, and 216 starts and runs the query every day. In another example, a securities trading strategy could be a persistent query. The persistent query controller can start the trading strategy query every morning before the market opened, for instance. It will be appreciated that 216 can work on times other than days. In some implementations, the controller may require its own clients to request that queries be started, stopped, etc. This can be done manually, or by scheduled (e.g., cron jobs). Some implementations can include “advanced scheduling” (e.g., auto-start/stop/restart, time-based repeat, etc.) within the controller.

The GUI/host workstation can include a user console 218 and a user query application 220. The user console 218 can be configured to connect to the persistent query controller 216. The user query application 220 can be configured to connect to one or more remote query dispatchers (e.g., 232) and one or more remote query processors (228, 230).

FIG. 3A is a diagram of an example query server host 320 (e.g., as described at 208, 210, and/or 106) in accordance with at least one embodiment. Query server host 320 can include a processor 324, a high speed memory (e.g., RAM) 326, another high speed memory (e.g., shared RAM with RQP and TDP) 336. Query server host 320 can access a medium access speed memory 346 (e.g., RAM managed by another host (actual or virtual) such as, for example, an intraday server (e.g., DIS 120 or LTDS 124)) and a slow access speed storage 355 (e.g., a file server with hard drive such as, for example, long term file server 108).

In operation, processor 324 can execute remote query processor 322 which stores/accesses data in high speed memory 326, high speed memory 336, medium access speed memory 346, and slow access speed storage 354. High speed memory 326 and high speed memory 336 can be memory on the same or different memory devices.

High speed memory 326 can contain one or more query update graphs 328, one or more table indexes 330, in memory data 332, and recent data cache 334. High speed memory 326 can request and retrieve data from one or more slow access speed storages 355 and/or from high speed memory 336.

High speed memory 336 can be memory that is shared with one or more remote query processors 322 and/or one or more table data cache proxies (e.g., TDCP 392 and 404, as shown in FIG. 3 and FIG. 4 respectively). High speed memory 336 can contain one or more data columns, for example, a symbol column data 338, a date column data 340, a time column data 342, and a quote column data 344. High speed memory 336 can exchange data with remote query processor 322, high speed memory 326, and/or medium access speed memory 346, and can request and receive data from slow access speed storage 355.

Medium access speed memory 346 can contain one or more data columns, for example, symbol column data 348, a date column data 350, a time column data 352, and a quote column data 354. Medium access speed memory 346 can exchange data with high speed memory 336 and can transmit data to a slow access speed storage 355. In some embodiments, medium access speed memory 346 is RAM that resides on a remote host, administered by a remote process (e.g., DIS 120, LTDS 124, or RUTS 142).

Slow access speed storage 355, for example, a file server with one or more hard drives, can contain persistent column data, for example, a symbol column 358, a date column 360, a time column 362, and a quote column 364. The one or more persisted column data 358-364 can be copied into medium speed solid state storage 356, for example, flash, to provide faster access for more frequently accessed data. In some embodiments, slow access speed storage 355 is used by long-term file server 108.

In some embodiments, remote query processor 322 can access a table having column data of two or more columns of the table stored in different memory devices and/or data servers. In some such embodiments, column data of a column (i.e., different rows or groups of rows) can also be stored in different memory devices and/or data servers. Processor 322 can store/access identifiers indicating where column data of the columns/rows is stored. Processor 322 can also store/access one or more table indexes associated with the table that identify for that table the valid portion(s) of the data referenced by the location identifier (e.g., the portions of the data which correspond to the rows of that table). For example, in some embodiments, a first portion of column data can be stored in shared memory with TDCP 336, a second portion of column data can be stored in medium speed memory 346, and a third portion of column data can be stored in slow speed storage 355, for the same or different columns of a table.

FIG. 3B is a diagram of an example query server host 370 (e.g., as described at 320, 208, 210, and/or 106) in accordance with at least one embodiment. Query server host 370 can contain one or more remote query processors (372, 374, 376) associated with one or more table data cache proxy clients (378, 380, 382), a shared memory 384 (e.g., as described at 336) that can exchange data (386, 388, 390) with table data cache proxy clients (378, 380, 382), and one or more table data cache proxies 392 that can exchange data with shared memory 384.

FIG. 4 is a diagram of an example computer data system and network 400 showing an example data distribution configuration in accordance with some implementations. System 400 includes local table data server (LTDS) 124, data import server (DIS) 120, remote user table server (RUTS) 142, and a host 402 (e.g., query server host 106, 208, 210, 320, and/or 370). Host 402 includes table data cache proxy (TDCP) 404, one or more remote query processors (RQP) 412/414, and one or more TDCP clients 408/410. TDCP includes a cache 406. In operation, each RQP 412/414 transmits data to and receives data from TDCP 404 via a corresponding one of TDCP clients 408/410, and TDCP 404 transmits data to and receives data from each of LTDS 124, DIS 120, and RUTS 142.

In some embodiments, TDCP 404 exports a composite table data service composed of multiple filtered remote table data services. In some embodiments, each of data sources 120, 124, and 142 exports a table data service via a messaging protocol and TDCP 404 is configured to export a composite table data service composed of the table data services of the data sources via a messaging protocol. The composite table data service of TDCP 404 can be composed of the services of data sources 120, 124, and 142 that are filtered and/or combined at a table-location level. A given “location” for a table may be provided by a single, non-composite service, but a client-visible table might be composed of locations from multiple underlying sources and the composite table data service of TDCP 404 provides data from the multiple underlying sources to a client (e.g., RQP 412/414 via TDCP clients 408/410), filtered and/or combined as appropriate.

In some embodiments, TDCP 404 is coupled to multiple of one or more of the different types of data sources 120, 124, and 142. In some such embodiments, the multiple data sources can provide the same data. The composite table data service of TDCP 404 can be configured to reconnect to any of the data sources that can provide the same data, and upon reconnection any in-progress requests are re-sent and any subscriptions are renewed.

In some embodiments, LTDS 124 provides access to un-merged real-time data from previous time periods (e.g., from the same storage systems to which current-period data is persisted by DIS 120). In some such embodiments, LTDS 124 can be used as a stop-gap (i.e., alternate data source) when issues prevent timely completion of the merge operations that transform, validate, and promote periodic data.

In some embodiments, the messaging protocol is identical for data sources 120, 124, and 142. In some such embodiments, the messaging protocol for TDCP 404 is identical to that of the data sources. In some embodiments, the messaging protocol of the data sources 120, 124, and 142 and/or the messaging protocol of TDCP 404 is built on top of one or more other networking protocols (e.g., TCP/IP).

In some embodiments, TDCP 404 is configured to serve as an aggregator of subscriptions by RQP 412/414 to the same metadata updates and/or requests for individual data and/or metadata items. TDCP 404 is configured to cache data and/or metadata received from data servers 120, 124, and 142 in cache 406.

In some embodiments, cache 406 comprises data blocks indexed by {{namespace, table name, table type}, {internal partition, column partition}, {column name, column file type, offset}} or, more generally, {table key}, {table location key}, {column block key}. Data blocks in cache 406 may be evicted (e.g., based on a modified LRU policy) at any time when they are not actively in-use. If more data becomes available (e.g., as implied by table size change notifications), the missing region of an already-cached data block can be read when a client requests the block in question. Successful data block read requests provide at least as much data as requested, but may provide more if the block has grown and the source can provide an additional suffix.

In some embodiments, cache 406 is indexed per-source. Each bottom-level table data service uses either the filesystem (which may be remote or local) or at most one actively-connected server, and maintains its own index of which location data it has. All caches within a given process generally share the same pool of free/managed data block memory-space, but this is configurable. The engine code deals with table locations, which have location-level metadata, per-column location-level metadata, and buffer stores associated with each relevant column file. The buffer store handles block indexing and presents the engine with byte-oriented access, which is then translated to cell-oriented access by an intermediate level of the engine code.

In embodiments, system 400 is configured such that TDCP 404 is authoritative for data stored in cache 406 from at least one of data sources 120, 124, and 142. Cache 406 includes data requested by the clients of TDCP 404, and different TDCPs with different attached clients/workloads will have different caches. Additionally, because of the underlying model for data updates used by data sources such as 120 and 124 in some embodiments (repeatable reads by virtue of append-only data changes, in the real-time system), if cache 406 has a data block from one of those data sources, it has the correct (i.e., authoritative) data for that data block. If it has a partial data block, the range that it has is correct data for that range. TDCP 404 therefore doesn't have to worry about having its data invalidated by upstream changes.

In some embodiments, connections between data sources 120, 124, and 142 and TDCP 404 are authenticated (e.g., using ACL permissions) and/or encrypted.

In some embodiments, TDCP 404 can communicate with TDCP clients 408-410 via an inter-process communication (IPC) mechanism (e.g., sockets, shared memory, memory mapped files, message passing, pipes, and/or message queues). For example, cache 406 can be stored in shared memory (e.g., shared memory 384 as shown in FIG. 3B). In some embodiments, the shared memory is System V IPC shared memory accessed with the various “shm_system” calls. In some embodiments, mixed IPC mechanisms may be used. For example, TDCP 404 can transmit data to one or more TDCP clients on a different host (actual or virtual) via one IPC mechanism (e.g., a socket/network) and provide data to one or more different TDCP clients via a different IPC mechanism (e.g., shared memory). In another example, TDCP 404 can communicate with data sources 120/124/142 using an IPC mechanism that is the same or different than that used for communications between TDCP 404 and TDCP clients 408-410.

Although not shown, in some embodiments, TDCP clients 408-410 and RQP 412-414 can be on a separate host (actual or virtual). In some such embodiments, data can be transmitted between TDCP 404 and TDCP clients 408-410 via a network.

In some embodiments, data may be transmitted between TDCP 404, data sources 120/124/142, TDCP clients 408-410, and RQP 412-414 using single and/or multipart messages.

In some embodiments, TDCP 404 and/or TDCP clients 408-410 maintain a separate cache (or a separate portion of the cache) for each RQP 412-414. In other embodiments, TDCP 404 and/or TDCP clients 408-410 can maintain separate and/or shared caches for RQP 412-414 (e.g., sharing a cache between two or more RQPs while maintaining a separate cache for a different RQP, or sharing one cache for all RQPs).

FIG. 5 is a flowchart of an example method 500 of processing a TDCP composite table data service request in accordance with some implementations. Processing begins at 502, where a remote query processor (RQP) (e.g., remote query processors 132-134, 322, 372-376, or 412-414 shown in FIG. 1, FIG. 3A, FIG. 3B, and FIG. 4, respectively) requests table (data and/or metadata) from a TDCP server (e.g., as shown in FIG. 3B or FIG. 4). Processing continues to 504.

At 504, the TDCP determines whether the requested table data/metadata is in the TDCP local state (or cache such as, for example, shared RAM 336, shared memory 384, or cache 406 shown in FIG. 3A, FIG. 3B, and FIG. 4, respectively). If so, processing continues to 510; otherwise, processing continues to 506.

At 506 the TDCP requests the latest state and subscribes to updates for the table from one or more appropriate data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142 shown in FIG. 1 and FIG. 4). In some embodiments, the TDCP is coupled to multiple data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142) and the TDCP selects one or more of the multiple data servers as the appropriate data servers for the request received at 502. In some embodiments, the one or more appropriate data servers can be selected based on a table type indicated in the request received at 502 (e.g., by determining which of the data servers match the table type and selecting those that match). In some embodiments, additional user tables can come from RUTS 142 and one more “user” filesystems mounted over NFS, performance log tables can be from locally-mounted filesystems, all of which may have independent services that can be composed by the TDCP and selected by the TDCP as one of the appropriate data servers.

At 508, the appropriate data servers distribute latest table state to the TDCP. The latest table state is received in response to the request(s) made by the TDCP at 506 and is stored in the TDCP cache. Processing continues to 510.

At 510, the TDCP filters/distributes the latest table state to the RQP. The latest table state can be filtered based on rules such as, for example, rules defining where data should come from (e.g., which data source is authoritative). Filtering can include removing metadata changes that aren't important to certain classes of downstream consumers (e.g., some TDCPs or RQPs might only want size changes, and/or might not want modifications after a certain time of day, and/or might not need to see all metadata fields). Additionally or alternatively, the TDCP can combine data related to two or more “downstream” table locations (with the same keys), and the filtering can include coalescing changes and only advertising them when (for example) a threshold (more than one, or all) number of the data sources being combined had advertised the same thing, thereby increasing the likelihood that data can be re-read promptly in the event of a subset of the data sources crashing or becoming partitioned away.

In some embodiments, the TDCP can be configured to optimize the performance of computer data access when multiple data servers each provide access to the same data by requesting the latest table state from two or more of the multiple data servers at 506 and by filtering the received/cached table state to generate a complete, non-duplicative composite table state that includes data/metadata from two or more of the multiple data servers. In such embodiments, the table state can include table locations and the TDCP can filter table locations to be distributed to the RQP to interleave table locations from the multiple data servers, thereby distributing subsequent data access across the multiple data servers. Processing continues to 512.

At 512, the TDCP listens to table updates from the data servers. Listening to table updates can include listening to table updates from data servers to which the TDCP has subscribed for updates. Processing continues to 514.

At 514, the TDCP distributes updates to subscribing RQP. The TDCP can operate as an aggregator of subscriptions for multiple RQP and upon receiving an update the TDCP can distribute the update to the subscribing RQP. For example, although not shown, two different RQP can subscribe to the TDCP to receive updates. In some embodiments, the updates can be filtered as described above at 510 before being distributed to subscribing RQP.

It will be appreciated that, although not shown, the subscribing RQP can cancel their subscription to stop receiving updates from the TDCP, that all subscriptions are cancelled for an RQP that disconnects, and that the TDCP may cancel its own data subscriptions and/or discard data it no longer needs for any RQP.

It will also be appreciated that 502-514 may be repeated in whole or in part. For example, 512-514 may be repeated to continuously provide table location updates to the subscribing RQP.

FIG. 6 is a flowchart of an example method 600 of processing a table location discovery request by a TDCP server (e.g., as shown in FIG. 3B or FIG. 4) in accordance with some implementations. Processing begins at 602, where a remote query processor (RQP) (e.g., remote query processors 132-134, 322, 372-376, or 412-414 shown in FIG. 1, FIG. 3A, FIG. 3B, and FIG. 4, respectively) requests table locations for a given table key from the TDCP. The table key can comprise a namespace, a table name, and a table type (e.g. user/system, periodic/historical). In some embodiments, table locations are keyed by path information such as, for example, internal partition and column partition. Processing continues to 604.

At 604, the TDCP determines whether the requested data is in the TDCP local state (or cache such as, e.g., shared RAM 336, shared memory 384, or cache 406 shown in FIG. 3A, FIG. 3B, and FIG. 4, respectively). If so, processing continues to 610; otherwise, processing continues to 606.

At 606 the TDCP requests table locations for the given table key from one or more appropriate data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142 shown in FIG. 1 and FIG. 4) and optionally subscribes for updates. In some embodiments, the TDCP is coupled to multiple data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142) and the TDCP selects one or more of the multiple data servers as the appropriate data servers for the request received at 602. In some embodiments, the one or more appropriate data servers can be selected based on the table type indicated by the table key (e.g., by determining which of the data servers match the table type and selecting those that match).

At 608. the TDCP receives table locations. The table locations are received in response to the request(s) made by the TDCP at 606 and are stored in the TDCP cache. Processing continues to 610.

At 610, the TDCP filters/distributes the table locations to the RQP. The table locations can be filtered based on rules such as, for example, rules defining where data should come from (e.g., which data source is authoritative). In some embodiments, the TDCP can be configured to optimize the performance of computer data access when multiple data servers each provide access to the same data by requesting table locations from two or more of the multiple data servers at 606 and by filtering the received/cached table locations to generate a complete, non-duplicative set of table locations that includes locations from two or more of the multiple data servers. In such embodiments, the TDCP can, for example, filter the table locations to be distributed to the RQP to interleave table locations from the multiple data servers, thereby distributing subsequent data access across the multiple data servers.

Additionally or alternatively, the TDCP can combine data received from data servers that provide access to the same data (e.g., by combining different portions to generate a complete, non-duplicative set as discussed above, by combining all data, or by including data received from one of the data servers and excluding data received from the other data servers), and the filtering can include coalescing changes and only advertising them when (for example) a threshold (more than one, or all) number of the data sources being combined had advertised the same thing, thereby increasing the likelihood that data can be re-read promptly in the event of a subset of the data sources crashing or becoming partitioned away. Processing continues to 612.

At 612, the TDCP listens to table updates from the data servers. Listening to table updates can include listening to table updates from data servers to which the TDCP has subscribed for updates. Processing continues to 614.

At 614, the TDCP distributes updates to subscribing RQP. The TDCP can operate as an aggregator of subscriptions for multiple RQP and upon receiving an update the TDCP can distribute the update to the subscribing RQP. For example, although not shown, two different RQP can subscribe to the TDCP to receive updates to table locations. In some embodiments, the updates can be filtered as described above at 610 before being distributed to subscribing RQP.

It will be appreciated that, although not shown, the subscribing RQP can cancel their subscription to stop receiving updates from the TDCP, that all subscriptions are cancelled for an RQP that disconnects, and that the TDCP may cancel its own data subscriptions and/or discard data it no longer needs for any RQP.

It will also be appreciated that 602-614 may be repeated in whole or in part. For example, 612-614 may be repeated to continuously provide table location updates to the subscribing RQP.

FIG. 7 is a flowchart of an example method 700 of processing a table location metadata retrieval request by a TDCP server (e.g., as shown in FIG. 3B or FIG. 4) in accordance with some implementations. Processing begins at 702, where a remote query processor (RQP) (e.g., remote query processors 132-134, 322, 372-376, or 412-414 shown in FIG. 1, FIG. 3A, FIG. 3B, and FIG. 4, respectively) requests table location metadata for a given table from the TDCP. The table location metadata can comprise size, modification time, validation status and validation completion time (e.g., validation being a process of ensuring that the data has passed proper quality checks), schema version used to generate the data, code version used to generate the data, user identifying information, and other metadata. Table location metadata can also include an “is valid” flag or an “is finished” flag to indicate that the data has been validated (e.g., that the data has passed proper quality checks). Processing continues to 704.

At 704, the TDCP determines whether the requested table location metadata is in the TDCP local state (or cache such as, e.g., shared RAM 336, shared memory 384, or cache 406 shown in FIG. 3A, FIG. 3B, and FIG. 4, respectively). If so, processing continues to 710; otherwise, processing continues to 706.

At 706 the TDCP requests table location metadata from one or more appropriate data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142 shown in FIG. 1 and FIG. 4) and subscribes for updates. In some embodiments, the TDCP is coupled to multiple data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142) and the TDCP selects one or more of the multiple data servers as the appropriate data servers for the request received at 702. In some embodiments, the one or more appropriate data servers can be selected based on a table type and/or table location indicated in the request received at 702 (e.g., by determining which of the data servers match the table type and/or table location and selecting those that match).

At 708. the TDCP receives table location metadata. The table location metadata is received in response to the request(s) made by the TDCP at 706 and are stored in the TDCP cache. Processing continues to 710.

At 710, the TDCP filters/distributes the table location metadata to the RQP. The table location metadata can be filtered based on rules such as, for example, rules defining where data should come from (e.g., which data source is authoritative). Filtering can include removing metadata changes that aren't important to certain classes of downstream consumers (e.g., some TDCPs or RQPs might only want size changes, and/or might not want modifications after a certain time of day, and/or might not need to see all metadata fields). Additionally or alternatively, the TDCP can combine data related to two or more “downstream” table locations (with the same keys), and the filtering can include coalescing changes and only advertising them when (for example) a threshold (more than one, or all) number of the data sources being combined had advertised the same thing, thereby increasing the likelihood that data can be re-read promptly in the event of a subset of the data sources crashing or becoming partitioned away. Processing continues to 712.

At 712, the TDCP listens to table updates from the data servers. Listening to table updates can include listening to table updates from data servers to which the TDCP has subscribed for updates. Processing continues to 714.

At 714, the TDCP distributes updates to subscribing RQP. The TDCP can operate as an aggregator of subscriptions for multiple RQP and upon receiving an update the TDCP can distribute the update to the subscribing RQP. For example, although not shown, two different RQP can subscribe to the TDCP to receive updates to table location metadata. In some embodiments, the updates can be filtered as described above at 710 before being distributed to subscribing RQP.

It will also be appreciated that, although not shown, the subscribing RQP can cancel their subscription to stop receiving updates from the TDCP, that all subscriptions are cancelled for an RQP that disconnects, and that the TDCP may cancel its own data subscriptions and/or discard data it no longer needs for any RQP.

It will also be appreciated that 702-714 may be repeated in whole or in part. For example, 712-714 may be repeated to continuously provide table location metadata updates to the subscribing RQP (e.g. sending updates to table size as it changes).

FIG. 8 is a flowchart of an example method 800 of processing a column location metadata retrieval request by a TDCP server (e.g., as shown in FIG. 3B or FIG. 4) in accordance with some implementations. Processing begins at 802, where a remote query processor (RQP) (e.g., remote query processors 132-134, 322, 372-376, or 412-414 shown in FIG. 1, FIG. 3A, FIG. 3B, and FIG. 4, respectively) requests column location metadata (e.g., grouping information, periodic/historical). Processing continues to 804.

At 804, the TDCP determines whether the requested column location metadata is in the TDCP local state (or cache such as, e.g., shared RAM 336, shared memory 384, or cache 406 shown in FIG. 3A, FIG. 3B, and FIG. 4, respectively). If so, processing continues to 810; otherwise, processing continues to 806.

At 806 the TDCP requests column location metadata from one or more appropriate data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142 shown in FIG. 1 and FIG. 4) and optionally subscribes for updates. For example, a subscriber could subscribe to receive updates to value indexes included in column metadata (e.g., for real-time/periodic/intraday data). In some embodiments, the TDCP is coupled to multiple data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142) and the TDCP selects one or more of the multiple data servers as the appropriate data servers for the request received at 802. In some embodiments, the one or more appropriate data servers can be selected based on the table type indicated by a table type and/or table location indicated in the request received at 802 (e.g., by determining which of the data servers match the table type and/or table location and selecting those that match).

At 808. the TDCP receives column location metadata. The column location metadata is received in response to the request(s) made by the TDCP at 806 and are stored in the TDCP cache. Processing continues to 810.

At 810, the TDCP filters/distributes the column location metadata to the RQP. The column location metadata can be filtered based on rules such as, for example, rules defining where data should come from (e.g., which data source is authoritative). Column location metadata can also be filtered to eliminate updates of a nature not needed/requested by certain downstream consumers (e.g., grouping/indexing changes if the RQP/query doesn't use them). In some embodiments, filtering at 810 can include removing metadata changes that aren't important to certain classes of downstream consumers (e.g., some TDCPs or RQPs might only want size changes, and/or might not want modifications after a certain time of day, and/or might not need to see all metadata fields). Additionally or alternatively, the TDCP can combine data related to two or more “downstream” table locations (with the same keys), and filtering at 810 can include coalescing changes and only advertising them when (for example) a threshold (more than one, or all) number of the data sources being combined had advertised the same thing, thereby increasing the likelihood that data can be re-read promptly in the event of a subset of the data sources crashing or becoming partitioned away. Processing continues to 812.

At 812, the TDCP listens to table updates from the data servers. Listening to table updates can include listening to table updates from data servers to which the TDCP has subscribed for updates. Processing continues to 814.

At 814, the TDCP distributes updates to subscribing RQP. The TDCP can operate as an aggregator of subscriptions for multiple RQP and upon receiving an update the TDCP can distribute the update to the subscribing RQP. For example, although not shown, two different RQP can subscribe to the TDCP to receive updates to column location metadata. In some embodiments, the updates can be filtered as described above at 810 before being distributed to subscribing RQP.

It will be appreciated that, although not shown, the subscribing RQP can cancel their subscription to stop receiving updates from the TDCP, that all subscriptions are cancelled for an RQP that disconnects, and that the TDCP may cancel its own data subscriptions and/or discard data it no longer needs for any RQP.

It will also be appreciated that 802-814 may be repeated in whole or in part. For example, 812-814 may be repeated to continuously provide column location metadata updates to the subscribing RQP (e.g. sending updated column size as it changes).

FIG. 9 is a flowchart of an example method 900 of processing a column file metadata retrieval request by a TDCP server (e.g., as shown in FIG. 3B or FIG. 4) in accordance with some implementations. Processing begins at 902, where a remote query processor (RQP) (e.g., remote query processors 132-134, 322, 372-376, or 412-414 shown in FIG. 1, FIG. 3A, FIG. 3B, and FIG. 4, respectively) requests column file metadata which can include column file size (e.g., for column files that aren't an exact multiple of table location size) from the TDCP. Processing continues to 904.

At 904, the TDCP determines whether the requested column file metadata is in the TDCP local state (or cache such as, e.g., shared RAM 336, shared memory 384, or cache 406 shown in FIG. 3A, FIG. 3B, and FIG. 4, respectively). If so, processing continues to 910; otherwise, processing continues to 906.

At 906 the TDCP requests column file metadata from one or more appropriate data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142 shown in FIG. 1 and FIG. 4) and optionally subscribes for updates. In some embodiments, the TDCP is coupled to multiple data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142) and the TDCP selects one or more of the multiple data servers as the appropriate data servers for the request received at 902. In some embodiments, the one or more appropriate data servers can be selected based on a column and/or column location/file indicated in the request received at 902 (e.g., by determining which of the data servers match the column and/or column location/file).

At 908. the TDCP receives column file metadata. The column file metadata is received in response to the request(s) made by the TDCP at 906 and are stored in the TDCP cache. Processing continues to 910.

At 910, the TDCP filters/distributes the column file metadata to the RQP. The column file metadata can be filtered based on rules such as, for example, rules defining where data should come from (e.g., which data source is authoritative). Column file metadata can be filtered, for example, to throttle the rate of change notification to the same frequency as other notifications such as table metadata (e.g., size) change notifications. Column file metadata can also be filtered to eliminate updates of a nature not needed/requested by certain downstream consumers. In some embodiments, filtering at 910 can include removing metadata changes that aren't important to certain classes of downstream consumers (e.g., some TDCPs or RQPs might not want modifications after a certain time of day, and/or might not need to see all metadata fields). Additionally or alternatively, the TDCP can combine data related to two or more “downstream” table locations (with the same keys), and filtering at 910 can include coalescing changes and only advertising them when (for example) a threshold (more than one, or all) number of the data sources being combined had advertised the same thing, thereby increasing the likelihood that data can be re-read promptly in the event of a subset of the data sources crashing or becoming partitioned away. Processing continues to 912.

At 912, the TDCP listens to table updates from the data servers. Listening to table updates can include listening to table updates from data servers to which the TDCP has subscribed for updates. Processing continues to 914.

At 914, the TDCP distributes updates to subscribing RQP. The TDCP can operate as an aggregator of subscriptions for multiple RQP and upon receiving an update the TDCP can distribute the update to the subscribing RQP. For example, although not shown, two different RQP can subscribe to the TDCP to receive updates to column file metadata. In some embodiments, the updates can be filtered as described above at 910 before being distributed to subscribing RQP.

It will be appreciated that, although not shown, the subscribing RQP can cancel their subscription to stop receiving updates from the TDCP, that all subscriptions are cancelled for an RQP that disconnects, and that the TDCP may cancel its own data subscriptions and/or discard data it no longer needs for any RQP.

It will also be appreciated that 902-914 may be repeated in whole or in part. For example, 912-914 may be repeated to continuously provide column file size updates to the subscribing RQP (e.g. sending updated column size as it changes).

FIG. 10 is a flowchart of an example method 1000 of processing a column file data retrieval request by a TDCP server (e.g., as shown in FIG. 3B or FIG. 4) in accordance with some implementations. Processing begins at 1002, where a remote query processor (RQP) (e.g., remote query processors 132-134, 322, 372-376, or 412-414 shown in FIG. 1, FIG. 3A, FIG. 3B, and FIG. 4, respectively) requests column file data from the TDCP. The client, and each intermediating service that cannot satisfy the request out of cache, requests the data for a block of binary data. The request includes the block size (which may be standardized at the service level), starting offset of the block within the column file, starting offset desired within the block, and minimum result length. More data (e.g., up to the maximum result length=block size-starting offset within the block) may be retrieved if available to prevent redundant subsequent requests. Processing continues to 1004.

At 1004, the TDCP determines whether the column file data is in the TDCP local state (or cache such as, e.g., shared RAM 336, shared memory 384, or cache 406 shown in FIG. 3A, FIG. 3B, and FIG. 4, respectively). If so, processing continues to 1010; otherwise, processing continues to 1006.

At 1006 the TDCP requests column file data from one or more appropriate data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142 shown in FIG. 1 and FIG. 4) and optionally subscribes for updates. In some embodiments, the TDCP is coupled to multiple data servers (e.g., LTDS 124, DIS 120, and/or RUTS 142) and the TDCP selects one or more of the multiple data servers as the appropriate data servers for the request received at 1002. In some embodiments, the one or more appropriate data servers can be selected based on the column file and/or the type of table that the column is a part of (e.g., by determining which of the data servers match the table type and selecting those that match). In some embodiments, when the TDCP has only a portion of the requested data in cache the TDCP can request whatever sub-range it doesn't have, and each request might actually get more data than they asked for in order to amortize away subsequent requests.

At 1008. the TDCP receives column file data. The column file data is received in response to the request(s) made by the TDCP at 1006 and are stored in the TDCP cache. Processing continues to 1010.

At 1010, the TDCP filters responses and/or distributes the column file data to the RQP. The column file data can be filtered based on rules such as, for example, rules defining where data should come from (e.g., which data source is authoritative when the data was requested from two or more sources). In some embodiments, the TDCP can be configured to optimize the performance of computer data access when multiple data servers each provide access to the same data by requesting different portions of the requested column file data from two or more of the multiple data servers at 606 and by filtering the responses to combine the different portions of column file data received from the multiple data servers into the column file data to be distributed to the RQP. In such embodiments, the TDCP can, for example, split the request across the multiple data servers, thereby distributing data access across the multiple data servers. In some embodiments, the TDCP or RQP may determine that requests for column file data follow a pattern (e.g., sequential access, striding, etc.) and prefetch one or more additional blocks or ranges of column file data from the appropriate data sources in order to enhance system performance by decreasing perceived latency and/or amortizing request costs. Processing continues to 1012.

At 1012, the TDCP listens to table updates from the data servers. Listening to table updates can include listening to table updates from data servers to which the TDCP has subscribed for updates. Processing continues to 1014.

At 1014, the TDCP distributes updates to subscribing RQP. The TDCP can operate as an aggregator of subscriptions for multiple RQP and upon receiving an update the TDCP can distribute the update to the subscribing RQP. For example, although not shown, two different RQP can subscribe to the TDCP to receive updates. In some embodiments, the updates can be filtered as described above at 1010 before being distributed to subscribing RQP.

It will be appreciated that, although not shown, the subscribing RQP can cancel their subscription to stop receiving updates from the TDCP, that all subscriptions are cancelled for an RQP that disconnects, and that the TDCP may cancel its own data subscriptions and/or discard data it no longer needs for any RQP.

It will also be appreciated that 1002-1014 may be repeated in whole or in part. For example, 1012-1014 may be repeated to continuously provide table location updates to the subscribing RQP.

FIG. 11 is a diagram of an example computing device 300 configured for table data cache proxy (TDCP) processing in accordance with at least one implementation. The computing device 300 includes one or more processors 302, operating system 304, computer readable medium 306 and network interface 308. The memory 306 can include table data cache proxy (TDCP) application 310 and a data section 312 (e.g., for storing caches, index data structures, column source maps, etc.).

In operation, the processor 302 may execute the application 310 stored in the memory 306. The application 310 can include software instructions that, when executed by the processor, cause the processor to perform operations for table data cache proxy processing in accordance with the present disclosure (e.g., performing one or more of 502-514, 602-614, 702-714, 802-814, 902-914, and/or 1002-1014 described above).

The application program 310 can operate in conjunction with the data section 312 and the operating system 304.

Although references have been made herein to tables and table data, it will be appreciated that the disclosed systems and methods can be applied with various computer data objects to, for example, provide flexible data routing and caching for such objects in accordance with the disclosed subject matter. For example, references herein to tables can include a collection of objects generally, and tables can include column types that are not limited to scalar values and can include complex types (e.g., objects).

It will be appreciated that the modules, processes, systems, and sections described above can be implemented in hardware, hardware programmed by software, software instructions stored on a nontransitory computer readable medium or a combination of the above. A system as described above, for example, can include a processor configured to execute a sequence of programmed instructions stored on a nontransitory computer readable medium. For example, the processor can include, but not be limited to, a personal computer or workstation or other such computing system that includes a processor, microprocessor, microcontroller device, or is comprised of control logic including integrated circuits such as, for example, an Application Specific Integrated Circuit (ASIC), a field programmable gate array (FPGA), a graphics processing unit (e.g., GPGPU or GPU) or the like. The instructions can be compiled from source code instructions provided in accordance with a programming language such as Java, C, C++, C#.net, assembly or the like. The instructions can also comprise code and data objects provided in accordance with, for example, the Visual Basic™ language, a specialized database query language, or another structured or object-oriented programming language. The sequence of programmed instructions, or programmable logic device configuration software, and data associated therewith can be stored in a nontransitory computer-readable medium such as a computer memory or storage device which may be any suitable memory apparatus, such as, but not limited to ROM, PROM, EEPROM, RAM, flash memory, disk drive and the like.

Furthermore, the modules, processes systems, and sections can be implemented as a single processor or as a distributed processor. Further, it should be appreciated that the steps mentioned above may be performed on a single or distributed processor (single and/or multi-core, or cloud computing system). Also, the processes, system components, modules, and sub-modules described in the various figures of and for embodiments above may be distributed across multiple computers or systems or may be co-located in a single processor or system. Example structural embodiment alternatives suitable for implementing the modules, sections, systems, means, or processes described herein are provided below.

The modules, processors or systems described above can be implemented as a programmed general purpose computer, an electronic device programmed with microcode, a hard-wired analog logic circuit, software stored on a computer-readable medium or signal, an optical computing device, a networked system of electronic and/or optical devices, a special purpose computing device, an integrated circuit device, a semiconductor chip, and/or a software module or object stored on a computer-readable medium or signal, for example.

Embodiments of the method and system (or their sub-components or modules), may be implemented on a general-purpose computer, a special-purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit element, an ASIC or other integrated circuit, a digital signal processor, a hardwired electronic or logic circuit such as a discrete element circuit, a programmed logic circuit such as a PLD, PLA, FPGA, PAL, GP, GPU, or the like. In general, any processor capable of implementing the functions or steps described herein can be used to implement embodiments of the method, system, or a computer program product (software program stored on a nontransitory computer readable medium).

Furthermore, embodiments of the disclosed method, system, and computer program product (or software instructions stored on a nontransitory computer readable medium) may be readily implemented, fully or partially, in software using, for example, object or object-oriented software development environments that provide portable source code that can be used on a variety of computer platforms. Alternatively, embodiments of the disclosed method, system, and computer program product can be implemented partially or fully in hardware using, for example, standard logic circuits or a VLSI design. Other hardware or software can be used to implement embodiments depending on the speed and/or efficiency requirements of the systems, the particular function, and/or particular software or hardware system, microprocessor, or microcomputer being utilized. Embodiments of the method, system, and computer program product can be implemented in hardware and/or software using any known or later developed systems or structures, devices and/or software by those of ordinary skill in the applicable art from the function description provided herein and with a general basic knowledge of the software engineering and computer networking arts.

Moreover, embodiments of the disclosed method, system, and computer readable media (or computer program product) can be implemented in software executed on a programmed general purpose computer, a special purpose computer, a microprocessor, or the like.

It is, therefore, apparent that there is provided, in accordance with the various embodiments disclosed herein, methods, systems and computer readable media for computer data distribution architecture.

Application Ser. No. 15/154,974, entitled “DATA PARTITIONING AND ORDERING” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,975, entitled “COMPUTER DATA SYSTEM DATA SOURCE REFRESHING USING AN UPDATE PROPAGATION GRAPH” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,979, entitled “COMPUTER DATA SYSTEM POSITION-INDEX MAPPING” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,980, entitled “SYSTEM PERFORMANCE LOGGING OF COMPLEX REMOTE QUERY PROCESSOR QUERY OPERATIONS” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,983, entitled “DISTRIBUTED AND OPTIMIZED GARBAGE COLLECTION OF REMOTE AND EXPORTED TABLE HANDLE LINKS TO UPDATE PROPAGATION GRAPH NODES” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,984, entitled “COMPUTER DATA SYSTEM CURRENT ROW POSITION QUERY LANGUAGE CONSTRUCT AND ARRAY PROCESSING QUERY LANGUAGE CONSTRUCTS” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,985, entitled “PARSING AND COMPILING DATA SYSTEM QUERIES” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,987, entitled “DYNAMIC FILTER PROCESSING” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,988, entitled “DYNAMIC JOIN PROCESSING USING REAL-TIME MERGED NOTIFICATION LISTENER” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,990, entitled “DYNAMIC TABLE INDEX MAPPING” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,991, entitled “QUERY TASK PROCESSING BASED ON MEMORY ALLOCATION AND PERFORMANCE CRITERIA” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,993, entitled “A MEMORY-EFFICIENT COMPUTER SYSTEM FOR DYNAMIC UPDATING OF JOIN PROCESSING” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,995, entitled “QUERY DISPATCH AND EXECUTION ARCHITECTURE” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,996, entitled “COMPUTER DATA DISTRIBUTION ARCHITECTURE” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,997, entitled “DYNAMIC UPDATING OF QUERY RESULT DISPLAYS” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,998, entitled “DYNAMIC CODE LOADING” (and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,999, entitled “IMPORTATION, PRESENTATION, AND PERSISTENT STORAGE OF DATA” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/155,001, entitled “COMPUTER DATA DISTRIBUTION ARCHITECTURE” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/155,005, entitled “PERSISTENT QUERY DISPATCH AND EXECUTION ARCHITECTURE” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/155,006, entitled “SINGLE INPUT GRAPHICAL USER INTERFACE CONTROL ELEMENT AND METHOD” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/155,007, entitled “GRAPHICAL USER INTERFACE DISPLAY EFFECTS FOR A COMPUTER DISPLAY SCREEN” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/155,009, entitled “COMPUTER ASSISTED COMPLETION OF HYPERLINK COMMAND SEGMENTS” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/155,010, entitled “HISTORICAL DATA REPLAY UTILIZING A COMPUTER SYSTEM” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/155,011, entitled “DATA STORE ACCESS PERMISSION SYSTEM WITH INTERLEAVED APPLICATION OF DEFERRED ACCESS CONTROL FILTERS” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/155,012, entitled “REMOTE DATA OBJECT PUBLISHING/SUBSCRIBING SYSTEM HAVING A MULTICAST KEY-VALUE PROTOCOL” and filed in the United States Patent and Trademark Office on May 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/351,429, entitled “QUERY TASK PROCESSING BASED ON MEMORY ALLOCATION AND PERFORMANCE CRITERIA” and filed in the United States Patent and Trademark Office on Nov. 14, 2016, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/813,142, entitled “COMPUTER DATA SYSTEM DATA SOURCE HAVING AN UPDATE PROPAGATION GRAPH WITH FEEDBACK CYCLICALITY” and filed in the United States Patent and Trademark Office on Nov. 14, 2017, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/813,127, entitled “COMPUTER DATA DISTRIBUTION ARCHITECTURE CONNECTING AN UPDATE PROPAGATION GRAPH THROUGH MULTIPLE REMOTE QUERY PROCESSORS” and filed in the United States Patent and Trademark Office on Nov. 14, 2017, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/813,119, entitled “KEYED ROW SELECTION” and filed in the United States Patent and Trademark Office on Nov. 14, 2017, is hereby incorporated by reference herein in its entirety as if fully set forth herein.

While the disclosed subject matter has been described in conjunction with a number of embodiments, it is evident that many alternatives, modifications and variations would be, or are, apparent to those of ordinary skill in the applicable arts. Accordingly, Applicants intend to embrace all such alternatives, modifications, equivalents and variations that are within the spirit and scope of the disclosed subject matter. 

What is claimed is:
 1. A computer database system with a plurality of memory devices optimized for ordered data and read-dominated workloads, the computer database system comprising: one or more processors; computer readable storage coupled to the one or more processors, the computer readable storage having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including: executing, at a query server computer, a database query, the query server computer comprising a query server memory device and being coupled to a plurality of data server computers, the executing comprising: providing a table data cache proxy operable to optimize performance of computer data access to the plurality of data server computers by: requesting a latest table state of a table from two or more of the plurality of data server computers, the two or more of the plurality of data server computers each providing access to at least a portion of the table's data, the portion of the table's data accessible from each data server computer being the same; filtering the latest table state to generate a complete, non-duplicative composite table state that includes one of data or metadata from the two or more of the plurality of data server computers, the non-duplicative composite table state including table locations, and the table data cache proxy being operable to filter table locations to be distributed to one or more remote query processors and distributing data access for the at least a portion of the table's data being the same across the two or more of the plurality of data server computers by interleaving table locations from the plurality of data server computers in the table locations included with the non-duplicative composite table state; and providing, from the table data cache proxy a single, non-composite service for table location; accessing a table comprising first and second columns, one or more first rows, and one or more second rows, wherein a given location for the table is provided by the single, non-composite service; storing one or more first location identifiers indicating where column data of the first column is stored and one or more second location identifiers indicating where column data of the second column is stored; and storing a table index comprising, for each location identifier of the one or more first location identifiers and the one or more second location identifiers, an index identifier indicating a valid portion of the data at the location indicated by the location identifier.
 2. The computer database system of claim 1, wherein the one or more first location identifiers indicate that data of the one or more first rows of the first column is stored in a first memory device of a first data server computer of the plurality of data server computers.
 3. The computer database system of claim 2, wherein the one or more second location identifiers indicate that data of the one or more first rows of the second column is stored in a second memory device of a second data server computer of the plurality of data server computers and that data of the one or more second rows of the second column is stored in a third memory device of a third data server computer of the plurality of data server computers.
 4. The computer database system of claim 3, wherein the executing further comprises: requesting one or more blocks of data of the one or more first rows from the first data server computer; and requesting one or more blocks of data of the one or more first rows from the second data server computer.
 5. The computer database system of claim 1, wherein a valid portion of the data includes data corresponding to the rows of the table and excludes data not corresponding to the rows of the table, such that data corresponding to a combination of the first location identifiers, the second location identifiers, and an index identifier comprises the table.
 6. The computer database system of claim 1, wherein a first index identifier and a second index identifier indicate a valid portion of the data at a respective location indicated by a corresponding location identifier.
 7. A method optimized for ordered data and read-dominated workloads of a computer data system with a plurality of memory devices, the method comprising: executing, at a query server computer, a database query, the query server computer comprising a query server memory device and being coupled to a plurality of data server computers, the executing comprising: providing a table data cache proxy operable to optimize performance of computer data access to the plurality of data server computers by: requesting a latest table state of a table from two or more of the plurality of data server computers, the two or more of the plurality of data server computers each providing access to at least a portion of the table's data, the portion of the table's data accessible from each data server computer being the same at least a portion of the table's data; filtering the latest table state to generate a complete, non-duplicative composite table state that includes one of data or metadata from the two or more of the plurality of data server computers, the non-duplicative composite table state including table locations, and the table data cache proxy being operable to filter table locations to be distributed to one or more remote query processors and distributing data access for the at least a portion of the table's data being the same across the two or more of the plurality of data server computers by interleaving table locations from the plurality of data server computers in the table locations included with the non-duplicative composite table state; and providing, from the table data cache proxy a single, non-composite service for table location; accessing a table comprising first and second columns, one or more first rows, and one or more second rows, wherein a given location for the table is provided by the single, non-composite service; storing one or more first location identifiers indicating where column data of the first column is stored and one or more second location identifiers indicating where column data of the second column is stored; and storing a table index comprising, for each location identifier of the one or more first location identifiers and the one or more second location identifiers, an index identifier indicating a valid portion of the data at the location indicated by the location identifier.
 8. The method of claim 7, wherein the one or more first location identifiers indicate that data of the one or more first rows of the first column is stored in a first memory device of a first data server computer.
 9. The method of claim 8, wherein the one or more second location identifiers indicate that data of the one or more first rows of the second column is stored in a second memory device of a second data server computer of the plurality of data server computers and that data of the one or more second rows of the second column is stored in a third memory device of a third data server computer of the plurality of data server computers.
 10. The method of claim 9, wherein the executing further comprises: requesting one or more blocks of data of the one or more first rows from the first data server computer; and requesting one or more blocks of data of the one or more first rows from the second data server computer.
 11. The method of claim 7, wherein a valid portion of the data includes data corresponding to the rows of the table and excludes data not corresponding to the rows of the table, such that data corresponding to a combination of the first location identifiers, the second location identifiers, and an index identifier comprises the table.
 12. The method of claim 7, wherein a first index identifier and a second index identifier indicate a valid portion of the data at a respective location indicated by a corresponding location identifier.
 13. A nontransitory computer readable medium having stored thereon software instructions that, when executed by one or more processors, cause the processors to perform operations including: executing, at a query server computer, a database query, the query server computer comprising a query server memory device and being coupled to a plurality of data server computers, the executing comprising: providing a table data cache proxy operable to optimize performance of computer data access to the plurality of data server computers by: requesting a latest table state from two or more of the plurality of data server computers; filtering the latest table state to generate a complete, non-duplicative composite table state that includes one of data or metadata from the two or more of the plurality of data server computers, the non-duplicative composite table state including table locations, and the table data cache proxy being operable to distribute data access across the two or more of the plurality of data server computers by interleaving table locations from plurality of data server computers in the table locations included with the non-duplicative composite table state; and providing, from the table data cache proxy a single, non-composite service for table location; accessing a table comprising first and second columns, one or more first rows, and one or more second rows, wherein a given location for the table is provided by the single, non-composite service; storing one or more first location identifiers indicating where column data of the first column is stored and one or more second location identifiers indicating where column data of the second column is stored; and storing a table index comprising, for each location identifier of the one or more first location identifiers and the one or more second location identifiers, an index identifier indicating a valid portion of the data at the location indicated by the location identifier.
 14. The nontransitory computer readable medium of claim 13, wherein the one or more first location identifiers indicate that data of the one or more first rows of the first column is stored in a first memory device of a first data server computer.
 15. The nontransitory computer readable medium of claim 14, wherein the one or more second location identifiers indicate that data of the one or more first rows of the second column is stored in a second memory device of a second data server computer of the plurality of data server computers and that data of the one or more second rows of the second column is stored in a third memory device of a third data server computer of the plurality of data server computers.
 16. The nontransitory computer readable medium of claim 15, wherein the executing further comprises: requesting one or more blocks of data of the one or more first rows from the first data server computer or the second data server computer; and requesting one or more blocks of data of the one or more second rows from the third data server computer.
 17. The nontransitory computer readable medium of claim 15, wherein first data is stored in the first data server computer in a first memory device in a first column-oriented configuration, wherein second data is stored in the second data server computer in a second memory device in a second column-oriented configuration, and wherein third data is stored in the third data server computer in a third memory device in a third column-oriented configuration.
 18. The nontransitory computer readable medium of claim 17, wherein the first memory device, the second memory device, and the third memory device are all different from each other.
 19. The nontransitory computer readable medium of claim 15, wherein a first index identifier and a second index identifier indicate a valid portion of the data at a respective location indicated by a corresponding location identifier.
 20. The nontransitory computer readable medium of claim 13, wherein the non-duplicative composite table state includes table locations, and wherein the table data cache proxy is operable to filter table locations to be distributed to one or more remote query processors to interleave table locations from the plurality of data server computers and distribute subsequent data access across the plurality of data server computers. 